Hourly Data

Dataset statistics

Number of variables	17
Number of observations	6966
Missing cells	0
Missing cells (%)	0.0%
Duplicate rows	0
Duplicate rows (%)	0.0%
Total size in memory	640.7 KiB
Average record size in memory	94.2 B

Variable types

Categorical	5
Numeric	9
Boolean	3

Alerts

`rental_date` has a high cardinality: 364 distinct values	High cardinality
`rental_month` is highly correlated with `rental_year`	High correlation
`rental_year` is highly correlated with `rental_month`	High correlation
`rental_month` is highly correlated with `rental_year`	High correlation
`rental_year` is highly correlated with `rental_month`	High correlation
`rental_hour` is highly correlated with `rental_year` and 1 other fields	High correlation
`rental_day` is highly correlated with `rental_year` and 1 other fields	High correlation
`rental_month` is highly correlated with `rental_year` and 1 other fields	High correlation
`rental_year` is highly correlated with `rental_hour` and 3 other fields	High correlation
`dayofweek_n` is highly correlated with `rain`	High correlation
`rain` is highly correlated with `rental_hour` and 4 other fields	High correlation
`working_day` is highly correlated with `dayofweek`	High correlation
`dayofweek` is highly correlated with `working_day`	High correlation
`rental_year` is highly correlated with `season`	High correlation
`season` is highly correlated with `rental_year`	High correlation
`rental_hour` is highly correlated with `peak` and 1 other fields	High correlation
`rental_month` is highly correlated with `rental_year` and 2 other fields	High correlation
`rental_year` is highly correlated with `rental_month` and 2 other fields	High correlation
`dayofweek_n` is highly correlated with `dayofweek` and 1 other fields	High correlation
`dayofweek` is highly correlated with `dayofweek_n` and 1 other fields	High correlation
`working_day` is highly correlated with `dayofweek_n` and 2 other fields	High correlation
`season` is highly correlated with `rental_month` and 2 other fields	High correlation
`peak` is highly correlated with `rental_hour` and 1 other fields	High correlation
`timesofday` is highly correlated with `rental_hour`	High correlation
`temp` is highly correlated with `rental_month` and 2 other fields	High correlation
`rental_date` is uniformly distributed	Uniform
`rental_hour` has 266 (3.8%) zeros	Zeros
`dayofweek_n` has 962 (13.8%) zeros	Zeros
`rain` has 6324 (90.8%) zeros	Zeros

Reproduction

Analysis started	2022-04-10 12:37:29.218385
Analysis finished	2022-04-10 12:38:02.054589
Duration	32.84 seconds
Software version	pandas-profiling v3.1.0
Download configuration	config.json

rental_date
Categorical

HIGH CARDINALITY
UNIFORM

Distinct	364
Distinct (%)	5.2%
Missing	0
Missing (%)	0.0%
Memory size	54.5 KiB

2021-12-15	24
2021-07-24	24
2022-02-05	24
2022-02-04	24
2021-06-27	23
Other values (359)	6847

Length

Max length	10
Median length	10
Mean length	10
Min length	10

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2021-03-01
2nd row	2021-03-01
3rd row	2021-03-01
4th row	2021-03-01
5th row	2021-03-01

Common Values

Value	Count	Frequency (%)
2021-12-15	24	0.3%
2021-07-24	24	0.3%
2022-02-05	24	0.3%
2022-02-04	24	0.3%
2021-06-27	23	0.3%
2021-09-04	23	0.3%
2021-08-01	23	0.3%
2022-02-16	23	0.3%
2022-02-11	23	0.3%
2021-09-11	23	0.3%
Other values (354)	6732	96.6%

Length

Histogram of lengths of the category

Value	Count	Frequency (%)
2021-12-15	24	0.3%
2022-02-04	24	0.3%
2021-07-24	24	0.3%
2022-02-05	24	0.3%
2021-09-04	23	0.3%
2021-08-01	23	0.3%
2022-02-16	23	0.3%
2022-02-11	23	0.3%
2021-09-11	23	0.3%
2022-01-27	23	0.3%
Other values (354)	6732	96.6%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

rental_hour
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	24
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	12.82931381

Minimum	0
Maximum	23
Zeros	266
Zeros (%)	3.8%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	0
5-th percentile	1
Q1	8
median	13
Q3	18
95-th percentile	22
Maximum	23
Range	23
Interquartile range (IQR)	10

Descriptive statistics

Standard deviation	6.3472844
Coefficient of variation (CV)	0.4947485496
Kurtosis	-0.8357483945
Mean	12.82931381
Median Absolute Deviation (MAD)	5
Skewness	-0.2775842676
Sum	89369
Variance	40.28801926
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
17	360	5.2%
18	358	5.1%
14	358	5.1%
13	356	5.1%
11	355	5.1%
15	354	5.1%
16	352	5.1%
12	352	5.1%
9	352	5.1%
10	349	5.0%
Other values (14)	3420	49.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	266	3.8%
1	168	2.4%
2	149	2.1%
3	130	1.9%
4	117	1.7%
5	134	1.9%
6	222	3.2%
7	293	4.2%
8	348	5.0%
9	352	5.1%

Value	Count	Frequency (%)
23	278	4.0%
22	306	4.4%
21	317	4.6%
20	344	4.9%
19	348	5.0%
18	358	5.1%
17	360	5.2%
16	352	5.1%
15	354	5.1%
14	358	5.1%

rental_day
Real number (ℝ_≥0)

HIGH CORRELATION

Distinct	31
Distinct (%)	0.4%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	15.64053976

Minimum	1
Maximum	31
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	1
5-th percentile	2
Q1	8
median	16
Q3	23
95-th percentile	29
Maximum	31
Range	30
Interquartile range (IQR)	15

Descriptive statistics

Standard deviation	8.689671829
Coefficient of variation (CV)	0.5555864414
Kurtosis	-1.177659354
Mean	15.64053976
Median Absolute Deviation (MAD)	7
Skewness	0.004253637489
Sum	108952
Variance	75.51039649
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=31)

Value	Count	Frequency (%)
24	240	3.4%
18	239	3.4%
11	236	3.4%
22	236	3.4%
12	236	3.4%
13	235	3.4%
23	235	3.4%
15	235	3.4%
17	234	3.4%
16	234	3.4%
Other values (21)	4606	66.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	224	3.2%
2	225	3.2%
3	225	3.2%
4	232	3.3%
5	231	3.3%
6	225	3.2%
7	234	3.4%
8	225	3.2%
9	232	3.3%
10	222	3.2%

Value	Count	Frequency (%)
31	106	1.5%
30	192	2.8%
29	210	3.0%
28	209	3.0%
27	233	3.3%
26	230	3.3%
25	227	3.3%
24	240	3.4%
23	235	3.4%
22	236	3.4%

rental_month
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	12
Distinct (%)	0.2%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	6.557565317

Minimum	1
Maximum	12
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	4
median	7
Q3	10
95-th percentile	12
Maximum	12
Range	11
Interquartile range (IQR)	6

Descriptive statistics

Standard deviation	3.437607482
Coefficient of variation (CV)	0.5242200901
Kurtosis	-1.196971805
Mean	6.557565317
Median Absolute Deviation (MAD)	3
Skewness	-0.04555345272
Sum	45680
Variance	11.8171452
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=12)

Value	Count	Frequency (%)
8	618	8.9%
10	617	8.9%
7	600	8.6%
6	599	8.6%
1	599	8.6%
9	585	8.4%
11	583	8.4%
5	566	8.1%
12	564	8.1%
3	555	8.0%
Other values (2)	1080	15.5%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	599	8.6%
2	544	7.8%
3	555	8.0%
4	536	7.7%
5	566	8.1%
6	599	8.6%
7	600	8.6%
8	618	8.9%
9	585	8.4%
10	617	8.9%

Value	Count	Frequency (%)
12	564	8.1%
11	583	8.4%
10	617	8.9%
9	585	8.4%
8	618	8.9%
7	600	8.6%
6	599	8.6%
5	566	8.1%
4	536	7.7%
3	555	8.0%

rental_year
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	54.5 KiB

2021	5823
2022	1143

Length

Max length	4
Median length	4
Mean length	4
Min length	4

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	2021
2nd row	2021
3rd row	2021
4th row	2021
5th row	2021

Common Values

Value	Count	Frequency (%)
2021	5823	83.6%
2022	1143	16.4%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
2021	5823	83.6%
2022	1143	16.4%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

holiday
Boolean

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

False	6684
True	282

Common Values
Chart

Value	Count	Frequency (%)
False	6684	96.0%
True	282	4.0%

dayofweek_n
Real number (ℝ_≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	3.031007752

Minimum	0
Maximum	6
Zeros	962
Zeros (%)	13.8%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	1
median	3
Q3	5
95-th percentile	6
Maximum	6
Range	6
Interquartile range (IQR)	4

Descriptive statistics

Standard deviation	1.989971351
Coefficient of variation (CV)	0.6565378625
Kurtosis	-1.241328444
Mean	3.031007752
Median Absolute Deviation (MAD)	2
Skewness	-0.02522150698
Sum	21114
Variance	3.959985976
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
5	1031	14.8%
4	1021	14.7%
2	997	14.3%
3	993	14.3%
6	988	14.2%
1	974	14.0%
0	962	13.8%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	962	13.8%
1	974	14.0%
2	997	14.3%
3	993	14.3%
4	1021	14.7%
5	1031	14.8%
6	988	14.2%

Value	Count	Frequency (%)
6	988	14.2%
5	1031	14.8%
4	1021	14.7%
3	993	14.3%
2	997	14.3%
1	974	14.0%
0	962	13.8%

dayofweek
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	7
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.3 KiB

Saturday	1031
Friday	1021
Wednesday	997
Thursday	993
Sunday	988
Other values (2)	1936

Length

Max length	9
Median length	7
Mean length	7.150301464
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Monday
2nd row	Monday
3rd row	Monday
4th row	Monday
5th row	Monday

Common Values

Value	Count	Frequency (%)
Saturday	1031	14.8%
Friday	1021	14.7%
Wednesday	997	14.3%
Thursday	993	14.3%
Sunday	988	14.2%
Tuesday	974	14.0%
Monday	962	13.8%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
saturday	1031	14.8%
friday	1021	14.7%
wednesday	997	14.3%
thursday	993	14.3%
sunday	988	14.2%
tuesday	974	14.0%
monday	962	13.8%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

working_day
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

True	4722
False	2244

Common Values
Chart

Value	Count	Frequency (%)
True	4722	67.8%
False	2244	32.2%

season
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct	4
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.1 KiB

Summer	1847
Autumn	1741
Spring	1704
Winter	1674

Length

Max length	6
Median length	6
Mean length	6
Min length	6

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Winter
2nd row	Winter
3rd row	Winter
4th row	Winter
5th row	Winter

Common Values

Value	Count	Frequency (%)
Summer	1847	26.5%
Autumn	1741	25.0%
Spring	1704	24.5%
Winter	1674	24.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
summer	1847	26.5%
autumn	1741	25.0%
spring	1704	24.5%
winter	1674	24.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

peak
Boolean

HIGH CORRELATION

Distinct	2
Distinct (%)	< 0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.0 KiB

False	4634
True	2332

Common Values
Chart

Value	Count	Frequency (%)
False	4634	66.5%
True	2332	33.5%

timesofday
Categorical

HIGH CORRELATION

Distinct	4
Distinct (%)	0.1%
Missing	0
Missing (%)	0.0%
Memory size	7.1 KiB

Afternoon	2132
Morning	1697
Evening	1673
Night	1464

Length

Max length	9
Median length	7
Mean length	7.191788688
Min length	5

Characters and Unicode

Total characters	0
Distinct characters	0
Distinct categories	0 ?
Distinct scripts	0 ?
Distinct blocks	0 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique	0 ?
Unique (%)	0.0%

Sample

1st row	Night
2nd row	Morning
3rd row	Morning
4th row	Morning
5th row	Morning

Common Values

Value	Count	Frequency (%)
Afternoon	2132	30.6%
Morning	1697	24.4%
Evening	1673	24.0%
Night	1464	21.0%

Length

Histogram of lengths of the category

Pie chart

Value	Count	Frequency (%)
afternoon	2132	30.6%
morning	1697	24.4%
evening	1673	24.0%
night	1464	21.0%

Most occurring characters

Value	Count	Frequency (%)
No values found.

Most occurring categories

Value	Count	Frequency (%)
No values found.

Most frequent character per category

Most occurring scripts

Value	Count	Frequency (%)
No values found.

Most frequent character per script

Most occurring blocks

Value	Count	Frequency (%)
No values found.

Most frequent character per block

rain
Real number (ℝ_≥0)

HIGH CORRELATION
ZEROS

Distinct	44
Distinct (%)	0.6%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	0.05961814528

Minimum	0
Maximum	10.3
Zeros	6324
Zeros (%)	90.8%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	0
5-th percentile	0
Q1	0
median	0
Q3	0
95-th percentile	0.3
Maximum	10.3
Range	10.3
Interquartile range (IQR)	0

Descriptive statistics

Standard deviation	0.3295040944
Coefficient of variation (CV)	5.526909515
Kurtosis	209.3934453
Mean	0.05961814528
Median Absolute Deviation (MAD)	0
Skewness	11.40675912
Sum	415.3
Variance	0.1085729482
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=44)

Value	Count	Frequency (%)
0	6324	90.8%
0.1	195	2.8%
0.2	86	1.2%
0.3	56	0.8%
0.4	44	0.6%
0.6	38	0.5%
0.5	28	0.4%
0.7	26	0.4%
0.8	21	0.3%
0.9	19	0.3%
Other values (34)	129	1.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
0	6324	90.8%
0.1	195	2.8%
0.2	86	1.2%
0.3	56	0.8%
0.4	44	0.6%
0.5	28	0.4%
0.6	38	0.5%
0.7	26	0.4%
0.8	21	0.3%
0.9	19	0.3%

Value	Count	Frequency (%)
10.3	1	< 0.1%
5.5	1	< 0.1%
5.2	1	< 0.1%
5.1	1	< 0.1%
4.9	1	< 0.1%
4.7	1	< 0.1%
4.6	1	< 0.1%
4.5	1	< 0.1%
4.2	1	< 0.1%
3.6	1	< 0.1%

temp
Real number (ℝ)

HIGH CORRELATION

Distinct	284
Distinct (%)	4.1%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	10.74239162

Minimum	-4
Maximum	26.3
Zeros	7
Zeros (%)	0.1%
Negative	54
Negative (%)	0.8%
Memory size	54.5 KiB

Quantile statistics

Minimum	-4
5-th percentile	2.6
Q1	7.025
median	10.6
Q3	14.5
95-th percentile	18.775
Maximum	26.3
Range	30.3
Interquartile range (IQR)	7.475

Descriptive statistics

Standard deviation	5.002159358
Coefficient of variation (CV)	0.4656467141
Kurtosis	-0.4056822749
Mean	10.74239162
Median Absolute Deviation (MAD)	3.7
Skewness	0.09826056311
Sum	74831.5
Variance	25.02159824
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
10.1	69	1.0%
8	66	0.9%
10.7	64	0.9%
10.6	64	0.9%
13.2	64	0.9%
7.6	63	0.9%
8.9	63	0.9%
8.7	62	0.9%
9.3	62	0.9%
11.3	60	0.9%
Other values (274)	6329	90.9%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
-4	1	< 0.1%
-3.4	1	< 0.1%
-3.3	1	< 0.1%
-2.9	3	< 0.1%
-2.8	1	< 0.1%
-2.6	1	< 0.1%
-2.5	1	< 0.1%
-2.3	1	< 0.1%
-2.1	1	< 0.1%
-2	2	< 0.1%

Value	Count	Frequency (%)
26.3	3	< 0.1%
26.2	1	< 0.1%
25.9	1	< 0.1%
25.7	2	< 0.1%
25.6	1	< 0.1%
25.4	3	< 0.1%
25.3	2	< 0.1%
25.2	1	< 0.1%
25.1	2	< 0.1%
25	1	< 0.1%

rhum
Real number (ℝ_≥0)

Distinct	69
Distinct (%)	1.0%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	80.54593741

Minimum	24
Maximum	100
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	24
5-th percentile	58
Q1	73
median	82
Q3	90
95-th percentile	97
Maximum	100
Range	76
Interquartile range (IQR)	17

Descriptive statistics

Standard deviation	11.91872934
Coefficient of variation (CV)	0.1479743079
Kurtosis	0.2126581319
Mean	80.54593741
Median Absolute Deviation (MAD)	8
Skewness	-0.7111188468
Sum	561083
Variance	142.0561091
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
87	256	3.7%
88	255	3.7%
82	251	3.6%
84	238	3.4%
89	232	3.3%
79	230	3.3%
85	223	3.2%
86	222	3.2%
83	222	3.2%
91	222	3.2%
Other values (59)	4615	66.3%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
24	1	< 0.1%
31	1	< 0.1%
32	1	< 0.1%
33	1	< 0.1%
36	1	< 0.1%
37	1	< 0.1%
38	1	< 0.1%
39	2	< 0.1%
40	4	0.1%
41	3	< 0.1%

Value	Count	Frequency (%)
100	100	1.4%
99	63	0.9%
98	88	1.3%
97	136	2.0%
96	148	2.1%
95	200	2.9%
94	188	2.7%
93	208	3.0%
92	200	2.9%
91	222	3.2%

wdsp
Real number (ℝ_≥0)

Distinct	33
Distinct (%)	0.5%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	8.811369509

Minimum	1
Maximum	35
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	1
5-th percentile	3
Q1	6
median	8
Q3	11
95-th percentile	17
Maximum	35
Range	34
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	4.38365003
Coefficient of variation (CV)	0.4974992849
Kurtosis	1.645777081
Mean	8.811369509
Median Absolute Deviation (MAD)	3
Skewness	1.002281537
Sum	61380
Variance	19.21638759
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=33)

Value	Count	Frequency (%)
7	722	10.4%
6	702	10.1%
8	660	9.5%
5	618	8.9%
9	573	8.2%
10	560	8.0%
4	508	7.3%
11	464	6.7%
12	346	5.0%
3	340	4.9%
Other values (23)	1473	21.1%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	31	0.4%
2	163	2.3%
3	340	4.9%
4	508	7.3%
5	618	8.9%
6	702	10.1%
7	722	10.4%
8	660	9.5%
9	573	8.2%
10	560	8.0%

Value	Count	Frequency (%)
35	2	< 0.1%
33	1	< 0.1%
31	1	< 0.1%
30	5	0.1%
29	4	0.1%
28	4	0.1%
27	4	0.1%
26	3	< 0.1%
25	6	0.1%
24	9	0.1%

count
Real number (ℝ_≥0)

Distinct	24
Distinct (%)	0.3%
Missing	0
Missing (%)	0.0%
Infinite	0
Infinite (%)	0.0%
Mean	4.754378409

Minimum	1
Maximum	26
Zeros	0
Zeros (%)	0.0%
Negative	0
Negative (%)	0.0%
Memory size	54.5 KiB

Quantile statistics

Minimum	1
5-th percentile	1
Q1	2
median	4
Q3	7
95-th percentile	11
Maximum	26
Range	25
Interquartile range (IQR)	5

Descriptive statistics

Standard deviation	3.442080321
Coefficient of variation (CV)	0.7239811442
Kurtosis	1.566956878
Mean	4.754378409
Median Absolute Deviation (MAD)	2
Skewness	1.177331745
Sum	33119
Variance	11.84791694
Monotonicity	Not monotonic

Histogram with fixed size bins (bins=24)

Value	Count	Frequency (%)
1	1229	17.6%
2	1000	14.4%
3	898	12.9%
4	775	11.1%
5	645	9.3%
6	597	8.6%
7	471	6.8%
8	368	5.3%
9	274	3.9%
10	208	3.0%
Other values (14)	501	7.2%

Minimum 5 values
Maximum 5 values

Value	Count	Frequency (%)
1	1229	17.6%
2	1000	14.4%
3	898	12.9%
4	775	11.1%
5	645	9.3%
6	597	8.6%
7	471	6.8%
8	368	5.3%
9	274	3.9%
10	208	3.0%

Value	Count	Frequency (%)
26	1	< 0.1%
24	2	< 0.1%
23	1	< 0.1%
21	1	< 0.1%
20	5	0.1%
19	8	0.1%
18	8	0.1%
17	16	0.2%
16	19	0.3%
15	32	0.5%

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Count
Matrix

A simple visualization of nullity by column.

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows

	rental_date	rental_hour	rental_day	rental_month	rental_year	holiday	dayofweek	working_day	season	peak	timesofday	temp	rhum	wdsp	count
0	2021-03-01	2	1	3	2021	False	Monday	True	Winter	False	Night	-1.2	98	4	1
1	2021-03-01	7	1	3	2021	False	Monday	True	Winter	True	Morning	2.1	100	4	3
2	2021-03-01	8	1	3	2021	False	Monday	True	Winter	True	Morning	5.1	98	5	1
3	2021-03-01	9	1	3	2021	False	Monday	True	Winter	True	Morning	5.7	98	5	4
4	2021-03-01	10	1	3	2021	False	Monday	True	Winter	True	Morning	6.7	94	6	4
5	2021-03-01	11	1	3	2021	False	Monday	True	Winter	False	Morning	7.4	91	8	4
6	2021-03-01	12	1	3	2021	False	Monday	True	Winter	False	Afternoon	6.9	88	8	8
7	2021-03-01	13	1	3	2021	False	Monday	True	Winter	False	Afternoon	9.3	84	8	11
8	2021-03-01	14	1	3	2021	False	Monday	True	Winter	False	Afternoon	9.3	80	9	11
9	2021-03-01	15	1	3	2021	False	Monday	True	Winter	True	Afternoon	8.3	79	11	10

Last rows

	rental_date	rental_hour	rental_day	rental_month	rental_year	holiday	dayofweek_n	dayofweek	working_day	season	peak	timesofday	rain	temp	rhum	wdsp	count
6956	2022-02-27	13	27	2	2022	False	6	Sunday	False	Winter	False	Afternoon	0.0	8.6	78	15	8
6957	2022-02-27	14	27	2	2022	False	6	Sunday	False	Winter	False	Afternoon	0.0	8.9	80	17	10
6958	2022-02-27	15	27	2	2022	False	6	Sunday	False	Winter	False	Afternoon	0.0	8.6	84	16	4
6959	2022-02-27	16	27	2	2022	False	6	Sunday	False	Winter	False	Afternoon	0.0	8.7	86	17	3
6960	2022-02-27	17	27	2	2022	False	6	Sunday	False	Winter	False	Afternoon	0.0	8.5	89	16	8
6961	2022-02-27	18	27	2	2022	False	6	Sunday	False	Winter	False	Evening	0.0	8.7	70	10	4
6962	2022-02-27	19	27	2	2022	False	6	Sunday	False	Winter	False	Evening	0.0	8.0	72	9	2
6963	2022-02-27	20	27	2	2022	False	6	Sunday	False	Winter	False	Evening	0.0	8.6	66	14	1
6964	2022-02-27	21	27	2	2022	False	6	Sunday	False	Winter	False	Evening	0.0	9.0	68	11	2
6965	2022-02-27	22	27	2	2022	False	6	Sunday	False	Winter	False	Evening	0.2	8.6	74	10	2

Overview

Variables

Common Values

Length

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Common Values

Length

Pie chart

Most occurring characters

Most occurring categories

Most frequent character per category

Most occurring scripts

Most frequent character per script

Most occurring blocks

Most frequent character per block

Interactions

Correlations

Spearman's ρ

Pearson's r

Kendall's τ

Cramér's V (φc)

Phik (φk)

Missing values

Sample

First rows

Last rows